Abstract: An object folding tool is leveraged in a digital medium environment. A two-dimensional (2D) representation of an unfolded object is obtained, and visual cues indicating folds for transforming the unfolded object into a folded object are detected. Based on the detected visual cues, a shape of the folded object is determined, and a three-dimensional (3D) representation of the folded object having the determined shape is generated. In one or more implementations, the 2D representation of the unfolded object and the 3D representation of the folded object are displayed concurrently on a display device.

Abstract: Generating vector representations of visual objects is leveraged in a digital medium environment. For instance, a raster-based visual input object is encoded into a global latent code and individual path latent codes for visual components of the raster visual object are extracted from the global latent code. The path latent codes are decoded and used to generate vector representations of the original raster versions of the visual components. The vector representations are rasterized and composited to generate an output object that simulates a visual appearance of the input object.

Abstract: Embodiments are disclosed for translating an image from a source visual domain to a target visual domain. In particular, in one or more embodiments, the disclosed systems and methods comprise a training process that includes receiving a training input including a pair of keyframes and an unpaired image. The pair of keyframes represent a visual translation from a first version of an image in a source visual domain to a second version of the image in a target visual domain. The one or more embodiments further include sending the pair of keyframes and the unpaired image to an image translation network to generate a first training image and a second training image. The one or more embodiments further include training the image translation network to translate images from the source visual domain to the target visual domain based on a calculated loss using the first and second training images.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure include receiving a raster image depicting a radial color gradient; compute a radial disk model for the radial color gradient, wherein the radial disk model defines a plurality of disks with centers aligned in a same direction; construct a vector graphics representation of the radial color gradient based on the radial disk model; and generate a vector graphics image depicting the radial color gradient based on the vector graphics representation.

Abstract: Embodiments are disclosed for reconstructing linear gradients from an input image that can be applied to another image. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving a raster image, the raster image including a representation of a linear color gradient. The disclosed systems and methods further comprise determining a vector representing a direction of the linear color gradient. The disclosed systems and methods further comprise analyzing pixel points along the direction of the linear color gradient to compute color stops of the linear color gradient. The disclosed systems and methods further comprise generating an output color gradient vector with the computed color stops of the linear color gradient, the output color gradient vector to be applied to a vector graphic.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure receive a raster image depicting a radial color gradient; compute an origin point of the radial color gradient based on an orthogonality measure between a color gradient vector at a point in the raster image and a relative position vector between the point and the origin point; construct a vector graphics representation of the radial color gradient based on the origin point; and generate a vector graphics image depicting the radial color gradient based on the vector graphics representation.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

Abstract: Embodiments are disclosed for receiving a target shape. The method may further include initializing a gradient mesh to a vector graphic having at least one node. The method may further include performing a constrained optimization of the vector graphic based on the target shape. The method may further include generating a stress metric based on a comparison of the constrained optimization and the target shape. The method may further include determining one or more unconstrained candidate vector graphics based on the stress metric. The method may further include selecting an improved vector graphic from the one or more unconstrained candidate vector graphics. The method may further include mapping the vector graphic to the improved vector graphic. The method may further include optimizing the improved vector graphic based on the target shape.

Abstract: Embodiments are disclosed for segmented image generation. The method may include receiving an input image and a segmentation mask, projecting, using a differentiable machine learning pipeline, a plurality of segments of the input image into a plurality of latent spaces associated with a plurality of generators to obtain a plurality of projected segments, and compositing the plurality of projected segments into an output image.

Abstract: Embodiments are disclosed for translating an image from a source visual domain to a target visual domain. In particular, in one or more embodiments, the disclosed systems and methods comprise a training process that includes receiving a training input including a pair of keyframes and an unpaired image. The pair of keyframes represent a visual translation from a first version of an image in a source visual domain to a second version of the image in a target visual domain. The one or more embodiments further include sending the pair of keyframes and the unpaired image to an image translation network to generate a first training image and a second training image. The one or more embodiments further include training the image translation network to translate images from the source visual domain to the target visual domain based on a calculated loss using the first and second training images.

Abstract: Methods and systems are provided for facilitating large-scale augmented reality in relation to outdoor scenes using estimated camera pose information. In particular, camera pose information for an image can be estimated by matching the image to a rendered ground-truth terrain model with known camera pose information. To match images with such renders, data driven cross-domain feature embedding can be learned using a neural network. Cross-domain feature descriptors can be used for efficient and accurate feature matching between the image and the terrain model renders. This feature matching allows images to be localized in relation to the terrain model, which has known camera pose information. This known camera pose information can then be used to estimate camera pose information in relation to the image.

Abstract: Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map.

Abstract: Generating vector representations of visual objects is leveraged in a digital medium environment. For instance, a raster-based visual input object is encoded into a global latent code and individual path latent codes for visual components of the raster visual object are extracted from the global latent code. The path latent codes are decoded and used to generate vector representations of the original raster versions of the visual components. The vector representations are rasterized and composited to generate an output object that simulates a visual appearance of the input object.

Abstract: Methods and systems are provided for facilitating large-scale augmented reality in relation to outdoor scenes using estimated camera pose information. In particular, camera pose information for an image can be estimated by matching the image to a rendered ground-truth terrain model with known camera pose information. To match images with such renders, data driven cross-domain feature embedding can be learned using a neural network. Cross-domain feature descriptors can be used for efficient and accurate feature matching between the image and the terrain model renders. This feature matching allows images to be localized in relation to the terrain model, which has known camera pose information. This known camera pose information can then be used to estimate camera pose information in relation to the image.

Abstract: Methods and systems disclosed herein relate generally to systems and methods for transforming document elements in response to modifications to a layout of a document. A layout-modification application identifies, from a first document having a first document layout, a first set of measurements of a document element and a first location of the document element within the first document. Based on an aspect-ratio difference between the first document layout and a second document layout, the layout-modification application selects a set of transformation rules that specify, for the document element, a second set of measurements and a second location within a second document. To select the particular set of transformation rules, the layout-modification application uses the determined aspect-ratio difference. The layout-modification application applies the selected set of transformation rules to the document element.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

Abstract: Certain aspects involve video inpainting via confidence-weighted motion estimation. For instance, a video editor accesses video content having a target region to be modified in one or more video frames. The video editor computes a motion for a boundary of the target region. The video editor interpolates, from the boundary motion, a target motion of a target pixel within the target region. In the interpolation, confidence values assigned to boundary pixels control how the motion of these pixels contributes to the interpolated target motion. A confidence value is computed based on a difference between forward and reverse motion with respect to a particular boundary pixel, a texture in a region that includes the particular boundary pixel, or a combination thereof. The video editor modifies the target region in the video by updating color data of the target pixel to correspond to the target motion interpolated from the boundary motion.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating a modified digital image by identifying patch matches within a digital image utilizing a Gaussian mixture model. For example, the systems described herein can identify sample patches and corresponding matching portions within a digital image. The systems can also identify transformations between the sample patches and the corresponding matching portions. Based on the transformations, the systems can generate a Gaussian mixture model, and the systems can modify a digital image by replacing a target region with target matching portions identified in accordance with the Gaussian mixture model.

Abstract: A user selects a set of photographs from a trip through an environment that he or she desires to present to other people. A collection of photographs, including the set of photographs captured during the trip optionally augmented with additional photographs obtained from another collection, are combined with a terrain model (e.g., a digital elevation model) to extract information regarding the geographic location of each of the photographs within the environment. The collection of photographs are analyzed, considering their geolocation information as well as the photograph content to register the photographs relative to one another. This information for the photographs is compared to the terrain model in order to accurately position the viewpoint for each photograph within the environment. A presentation of the selected photographs within the environment is generated that displays both the selected photographs and synthetic data filled in beyond the edges of the selected photographs.

Abstract: Techniques are disclosed for neural network based interpolation of image textures. A methodology implementing the techniques according to an embodiment includes training a global encoder network to generate global latent vectors based on training texture images, and training a local encoder network to generate local latent tensors based on the training texture images. The method further includes interpolating between the global latent vectors associated with each set of training images, and interpolating between the local latent tensors associated with each set of training images. The method further includes training a decoder network to generate reconstructions of the training texture images and to generate an interpolated texture based on the interpolated global latent vectors and the interpolated local latent tensors. The training of the encoder and decoder networks is based on a minimization of a loss function of the reconstructions and a minimization of a loss function of the interpolated texture.